Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

Abstract

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.

Accepted for Technometrics

References in corpus (6)

• XGBoost: A Scalable Tree Boosting System
• Optimal Subsampling for Large Sample Logistic Regression
• Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
• A Statistical Perspective on Algorithmic Leveraging
• Optimal Subsampling Algorithms for Big Data Regressions
• Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms